Businesses and organizations, for instance telephone companies, banks, governments, and educational institutions, store databases that grow in size with time (e.g., as telephone companies acquire more customers or as educational institutions acquire more students and alumni). The databases are used to generate results to queries. For example, a bank database may be queried for transactions on account number XXX dated between Jan. 1, 2014, and Jan. 31, 2014. However, in some cases, as a volume of data stored by a database increases, queries may become slower, making the database unable to respond to certain queries within a reasonable amount of time. In some cases, the database may be replaced with a more modern database that operates more efficiently. However, replacing an old database with a more modern database may not be desirable due to the expense, in terms of time, money, and potential errors, of reprogramming a large amount of software for interfacing with the old database. As the foregoing illustrates, an approach to increase query processing speed and efficiency of a database without replacing the database may be desirable.